System and method for using apparent size and orientation of an object to improve video-based tracking in regularized environments

ABSTRACT

A system and method for optimizing video-based tracking of an object of interest are provided. A video of a regularized motion environment that comprise multiple video frames is acquired and an initial instance of an object of interest in one of the frames is then detected. An expected size and orientation of the object of interest as a function of the location of the object is then determined. The location of the object of interest is then determined in a next subsequent frame using the expected size and orientation of the object of interest.

TECHNICAL FIELD

The presently disclosed embodiments are directed toward methods andsystems of the transportation arts, tracking arts, video processingarts, predictive arts, and the like. More particularly, the teachingsdisclosed herein are applicable to methods and systems whereinvideo-based tracking of objects of interest in a regularized environmentis optimized.

BACKGROUND

The proliferation of traffic and surveillance cameras and the increasingneed for automated video analytics technologies have brought the topicof object tracking to the forefront of computer vision research.Real-world scenarios present a wide variety of challenges to existingobject tracking algorithms including occlusions, changes in sceneillumination, conditions and object appearance (color, shape,silhouette, salient features, etc.), as well as camera shake. Whilesignificant research efforts have been devoted to solving the generalproblem of robustly tracking groups of objects under a wide range ofconditions, the environments encountered in traffic and surveillancesituations are typically limited in scope with respect to directions andspeeds at which objects move. Examples of implementations that rely onrobust object tracking include video-based parking management andvideo-based vehicle speed estimation, measuring total experience time inretail spaces, and the like.

The aforementioned real-world scenarios present a wide variety ofchallenges to existing object tracking algorithms. An example of such ascenario is the use of a fish eye camera to determine “total experiencetime” of a vehicle in a drive-thru setting, i.e., an ultra-wide-anglelens that produces a hemispheric view of a scene created via theintroduction of a lens that has a shape and index of refraction thatcaptures all light forward of the camera and focuses it on the CCD chip.Two key issues that affect performance of appearance-based objecttracking in video streams are (i) change in apparent size of an objectdue to perspective and/or distortion, and (ii) change in appearance ofan object due to its orientation relative to the camera. For example,due to the projective nature of a camera, objects farther away from thecamera appear smaller than objects closer by; this applies to bothrectilinear and fisheye lens cameras. In addition, fisheye lensesusually introduce extreme barrel distortion in order to achieve wideangles of view. Barrel distortion results in spatially varying imagemagnification, wherein the degree of magnification decreases with anobject's distance to the camera's optical axis. As another example,objects that are longer along one dimension than along others and thatchange orientation as they traverse the field of view of the camera areperceived to go through changes in aspect ratio, even in the absence oflens distortion.

While fisheye distortion is an extreme case of barrel distortion,usually associated with wide angle imaging systems, other types ofdistortion also occurs in imaging systems. For instance, telephotolenses often possess pincushion distortion, where magnificationincreases with distance from the optical axis. A zoom lens, as thoseused in common PTZ (Pan-Tilt-Zoom) surveillance systems, can operatealong a continuum from wide angle to normal (rectilinear) to telephoto,and possess respective distortions. Anamorphic optical systems may beused to form a panoramic view of a scene, where the distortion willdiffer in perpendicular directions.

Current attempts to estimate object size and orientation in addition toobject location can be error-prone and may have increased computationalcomplexity due to the higher-dimensional optimization space inprojective and optically induced distortion.

Thus, it would be advantageous to provide an efficient system and methodfor video-based tracking of an object of interest that exploits theregularized conditions present in transportation scenarios to achieverobust and computationally efficient tracking that has objectorientation and size awareness.

INCORPORATION BY REFERENCE

The following references, the disclosures of which are incorporatedherein by reference, in their entirety, are mentioned.

-   -   G. Bradski, Computer Vision Face Tracking for Use in a        Perceptual User Interface, Intel Technology Journal Q2 1998.    -   J. Ning, L. Zhang, D. Zhang and C. Wu, Scale and Orientation        Adaptive Mean Shift Tracking, Institution of Engineering and        Technology Computer Vision, January 2012.    -   D. Comaniciu et al., Real Time Tracking of Non-Rigid Objects        using Mean Shift, in Proc. IEEE CVPR 2000.    -   M. Isard and A. Blake, Contour Tracking by Stochastic        Propagation of Conditional Density, In. Proc. Euro. Conf.        Computer Vision, 1996.    -   K. Smith et al., Evaluating Multi-Object Tracking, Workshop on        Empirical Evaluation Methods in Computer Vision, 2005.    -   J. Shi and C. Tomasi, Good Features to Track, IEEE Conference on        Computer Vision and Pattern Recognition, 1994.    -   C. Hue et al., Tracking Multiple Objects with Particle        Filtering, IEEE Transactions on Aerospace and Electronic        Systems, Vol. 38, No. 3, July 2002.    -   K. Okuma, et al., A Boosted Particle Filter: Multitarget        Detection and Tracking, Lecture Notes in Computer Science,        Volume 3021, 2004,    -   D. Ross et al., Incremental Learning for Robust Visual Tracking,        Neural Information Processing Systems 17, MIT Press, 2005.

BRIEF DESCRIPTION

In one aspect of the exemplary embodiment, a method for optimizingvideo-based tracking of an object of interest is provided. The methodincludes acquiring a video of a regularized motion environmentcomprising a plurality of video frames. The method also includesdetecting an initial instance of at least one object of interest in theplurality of video frames including a location thereof, and determiningan expected size and an expected orientation of the at least one objectof interest as a function of the location. In addition, the methodincludes localizing the at least one object of interest in at least onesubsequent video frame responsive to the determined size andorientation. A computer processor performs the acquiring, detecting,generating, determining, and/or localizing.

In another aspect, a system for optimizing video-based tracking of anobject of interest is provided. The system includes a video acquisitionunit configured for acquiring a video of a regularized motionenvironment in memory, the video comprising a plurality of frames. Thesystem also includes an object detection unit configured for detectingan initial instance of an object of interest a frame of the plurality ofvideo frames, and an object characterization unit configured forestablishing a target object representation of the detected instance ofthe object of interest. Additionally, the system includes an objectlocalization unit configured for determining a location of the object ofinterest in the frame in accordance with the target representation ofthe detected instance of the object of interest. The system furtherincludes an object size and orientation unit configured for estimating asize and an orientation of the object of interest in a next subsequentframe as a function of the determined location. Furthermore, the systemincludes a processor which implements at least one of the videoacquisition unit, the object detection unit, the object characterizationunit, the object localization unit, and the object size and orientationunit.

In another aspect, a computer-implemented method for optimizingvideo-based tracking of an object of interest is provided. Thecomputer-implemented method includes generating a binary mask of aninstance of a detected object of interest in one of a plurality of videoframes, and establishing a target object representation of the detectedinstance of the object of interest in accordance with the generatedbinary mask. In addition, the computer-implemented method includesdetermining a location of the object of interest in the frame inaccordance with the target representation of the detected instance ofthe object of interest, and estimating a size and an orientation of theobject of interest as a function of the location. Thecomputer-implemented further includes localizing the object of interestin a next subsequent frame responsive to the estimated size andorientation.

In another aspect, a method for optimizing video-based tracking of anobject of interest is provided. The method includes acquiring a video ofa regularized motion environment comprising a plurality of video frames,and detecting an initial instance of at least one object of interest inan initial video frame of the plurality of video frames includingdetection of a location thereof. The method further includes localizingthe at least one object of interest in a plurality of subsequent videoframes, and determining an object trajectory of the at least one objectof interest localized in the plurality of subsequent video frames.Furthermore, the method includes determining an expected size and anexpected orientation of the at least one object of interest as afunction of the determined trajectory, and localizing the at least oneobject of interest in at least one of the plurality of subsequent videoframes based on the determined expected size and expected orientation. Acomputer processor performs at least one of the acquiring, detecting,localizing, determining, and localizing.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The following is a brief description of the drawings, which arepresented for the purposes of illustrating the exemplary embodimentsdisclosed herein and not for the purposes of limiting the same.

FIG. 1 is a functional block diagram of a video-based system foroptimizing tracking an object of interest in accordance with one aspectof the exemplary embodiment.

FIG. 2 is a functional block diagram of the interaction of components ofthe video-based system for optimizing tracking an object of interestshown in FIG. 1 in accordance with one aspect of the exemplaryembodiment.

FIG. 3 is an illustration of a sample video frame captured with thevideo acquisition for use in the video-based system for optimizingtracking an object of interest in accordance with one aspect of theexemplary embodiment.

FIGS. 4A-4F are illustrations of binary outputs and corresponding videoframes from the object detection unit of the video-based system foroptimizing tracking an object of interest in accordance with one aspectof the exemplary embodiment.

FIG. 5 is an illustration of a histogram corresponding to the objectdetected in FIGS. 4A-4F.

FIGS. 6A-6E are illustrations of kernel size, location, and orientationas used in the video-based system for optimizing tracking an object ofinterest in accordance with one aspect of the exemplary embodiment.

FIG. 7A is an illustration of a video camera viewing an abstraction of avehicle in accordance with one aspect of the exemplary embodiment.

FIG. 7B is an illustration of a mapping of corners of the abstractiondepicted in FIG. 7A to an image plane in accordance with one aspect ofthe exemplary embodiment.

FIGS. 8A-8B are illustrations of pseudo-colored maps respectivelyillustrating apparent size and orientation of an object of interestillustrated in FIGS. 3-7B.

FIG. 9 is a flowchart that illustrates one aspect of the method foroptimizing video-based tracking of an object of interest according to anexemplary embodiment.

FIG. 10 is a flowchart that illustrates another aspect of the method foroptimizing video-based tracking of an object of interest according to anexemplary embodiment.

DETAILED DESCRIPTION

One or more embodiments will now be described with reference to theattached drawings, wherein like reference numerals are used to refer tolike elements throughout. Aspects of exemplary embodiments related tosystems and methods for video-based tracking of objects of interest aredescribed herein. In addition, example embodiments are presentedhereinafter referring to tracking an object of interest in a regularizedmotion environment, such as tracking vehicles in a parking lot, on ahighway, on a road, etc., or people in a building, in a park, on asidewalk, etc., from acquired video, however application of the systemsand methods set forth herein can be made to other areas of tracking orimaging operations.

According to one embodiment, there are provided systems and methodswhich extend object tracking via exploitation of a priori and/or learnedknowledge of object size and orientation in a regularized motionenvironment in order to achieve robust and computationally efficienttracking. The systems and methods comprise the following modules orunits: (1) a video acquisition module that captures or otherwisereceives video of the area being monitored, (2) an object detectionmodule that detects an initial instance of an object of interest in theincoming video; (3) an object characterization module that establishes atarget object representation; (4) an object localization module thatdetermines the location of the object being tracked on a frame-by-framebasis. The systems and methods set forth herein further include (5) anobject size and orientation determination module that relays, e.g.,provides feedback, on the size and orientation object information tomodules (2), (3) and (4) as a function of the object location determinedby module (4) as well as learned or manually input size and orientationdata. According to one aspect, the object size and orientation unit cancomprehend the geometry and orientation of the object to make anaccurate estimate of the detected object size.

Referring now to FIG. 1, there is shown a functional block diagram of avideo-based system 100 for tracking an object of interest in accordancewith one aspect of the subject disclosure. It will be appreciated thatthe various components depicted in FIG. 1 are for purposes ofillustrating aspects of the exemplary embodiment, and that other similarcomponents, implemented via hardware, software, or a combinationthereof, are capable of being substituted therein.

As shown in FIG. 1, the searching system 100 includes a computer systemrepresented generally at 102, which is capable of implementing theexemplary method described below. It will be appreciated that whileshown with respect to the computer system 102, any suitable computingplatform may be utilized in accordance with the systems and methods setforth herein. The exemplary computer system 102 includes a processor104, which performs the exemplary method by execution of processinginstructions 108 which are stored in memory 106 connected to theprocessor 104, as well as controlling the overall operation of thecomputer system 102.

The instructions 108 include a video acquisition unit 110 operable toacquire video 138 of a scene of interest from an associated imagecapture device 134 via a suitable communications link 136, e.g., a videocamera, still camera, etc. Suitable examples of such image capturedevices 134 may include, for example, CMOS, CCD, and other types ofcameras capable of recording or capturing moving images. According toone embodiment, the video acquisition unit 110 may be emplaced in asuitable regularized motion environment 141, e.g., a parking lot, streetcorner, thoroughfare, highway, or the like, the environment 141 having aset of rules 156 corresponding thereto. It will be appreciated thatwhile illustrated in FIG. 1 as being directly coupled to the computersystem 102, the image capture device 134 may be in communication withthe computer system 102 via a communications network (not shown), suchas, for example, a virtual local area network, a wide area network, apersonal area network, a local area network, the Internet, an intranet,or any suitable combination thereof. The communications link 136 may beimplemented as, for example, the public-switched telephone network, aproprietary communications network, infrared, optical, or other suitablewired or wireless data communications channel.

The image capture device 134 may be implemented as a video camera incommunication with the video acquisition unit 110 to facilitatecapturing or otherwise receiving video 138 of the area of interest.Alternatively, previously captured and stored video can be read from adatabase 128. It will be appreciated that in accordance with the systemsand methods set forth herein, specific requirements in terms of spatialor temporal resolutions may not be needed. However, traditionalsurveillance cameras are typically IP cameras with pixel resolutions ofVGA and above (640×480) and frame rates of 15 fps and above. It willtherefore be appreciated that the systems and methods set forth hereinare capable of operations using a plurality of different pixelresolutions and different frame rates. It will further be appreciatedthat a fisheye camera can provide a large field of view of a scene, butat the expense of suffering from large changes in the size of the objectas it moves through the scene due to the aforementioned lens distortionsassociated with wide angles of view. In addition, image capture deviceinformation 158, e.g., the frame rate, position of device 134, angle,lens-type, and the like, may be utilized by the video acquisition unit110 or other unit in the operations set forth below. FIG. 3 illustratesa sample video frame 300 captured with the video camera 134 containingan example area of interest 302 test area used for demonstrationpurposes, e.g., a parking lot.

The instructions 108 of the system 100 further include an objectdetection unit 112 that is configured to detect an initial instance ofan object of interest 140 in the incoming video 138, i.e., the video 138captured (from video camera 134) or obtained (from the database 140) bythe video acquisition unit 110. In accordance with one embodiment, adouble difference technique followed by morphological operations may beimplemented by the object detection unit 112 to detect the initialinstance of an object of interest 140 in the incoming video 138. Themorphological operations discard objects in motion with size andorientation outside pre-determined ranges determined by the object sizeand orientation determination 118, discussed in detail below. The outputof the operation is a binary mask 142 with the same pixel dimensions asthe input video 138, and having values equal to 0 where nomotion/foreground object is detected and values equal to 1 at pixellocations where the contrary is true.

In accordance with another embodiment, background estimation andsubtraction may be used for foreground object detection, which requiresestimation of the stationary scene background, followed by subtractionor comparison between the estimated background and the current frame,coupled with morphological operations to isolate blobs of theappropriate size. A background estimate can comprise an image obtained,for example, by performing a pixel-wise running or weighted average, orpixel-wise median computation of incoming video frames; alternatively, abackground estimate can comprise a set of pixel-wise statistical modelsdescribing the historical behavior of pixel values. When subtracting acurrent frame with a background estimate, pixels that are above apre-determined threshold are deemed to belong to the foreground; whencomparing a current frame with a background estimate, pixels that aredeemed not to fit their corresponding statistical model are deemed tobelong to the foreground. The output of such approach is a binary mask142, similar to the output by the double difference technique.

In accordance with one embodiment, the object detection unit 112 may beconfigured to detect an initial instance of an object of interest 140via one or more external inputs. That is, the initial instance of anobject of interest 140 in a field of view may be pre-determined basedupon the position of an entryway (gate), a sensor/ticketing booth, orthe like. In such an embodiment, the initial detection of the object ofinterest 140 would be ascertained upon the object 140 activating a gate(not shown) or triggering a sensor (not shown), such that the initialdetection could occur prior to activation of video camera 134 to beginacquiring video 138 of the environment 141. Examples of triggeringsensors include roadway sensors such as, but not limited to, pressurehoses, piezoelectric sensors and induction coils that are physicallylaid out on the or underneath the surface of the road. Otherremote-sensing systems such as radar- and laser-based systems can beemployed. It will be appreciated that such an embodiment is capable ofimplementation in accordance with the systems and methods set forthherein and as explained in greater detail below.

FIGS. 4A-4F are an illustration of an example usage of size andorientation awareness in motion detection processes. The example ofFIGS. 4A-4F depict two objects of interest 140, e.g., vehicles, movingaround a scene, e.g., a parking lot, which is being monitored by asuitable video acquisition unit 110 inclusive of a video camera 134,e.g., a fisheye camera. As illustrated in FIGS. 4A-4F, the apparent sizeand orientation of the vehicles 140 change drastically. FIGS. 4A, 4C,and 4E show binary masks 142 corresponding to the input frames 144 fromFIGS. 4B, 4D, and 4F, respectively. The motion blobs 400 and 402depicted on the binary output 142, i.e., the binary mask 404 in FIG. 4A(corresponding to the video frame 405 of FIG. 4F), are 892 and 967pixels in size and are at an orientation of 19° and 7°, respectively. Incontrast, the blobs 406 and 408 in the mask 410 from FIG. 4C(corresponding to the video frame 411 of FIG. 4F) are 1,459 and 1,507pixels in size and are at an orientation of −26° and −37°, respectively.Lastly, the blobs 412 and 414 in the mask 416 from FIG. 4E(corresponding to the video frame 417 of FIG. 4F) are 32,462 and 11,186pixels in size and are at an orientation of −3° and 25°, respectively.

To achieve the appropriate selectivity of moving objects 140 accordingto their size, orientation and location, the object detection unit 112forwards the pixel coordinates corresponding to the detectedforeground/moving object 140 to the size and orientation determinationunit 118. In accordance with one embodiment, the size and orientationdetermination unit 118 (which is aware of the predominant object sizeand orientation of an object 140 as a function of location) creates therequired structuring elements 164 for the morphological operationsrelated with the computation of the foreground/motion binary mask, e.g.,404, 410, 416. It will be appreciated that the morphological operationsperform hole-filling in masks that result from the initial thresholdingoperation, as well as removal of identified objects with sizes and/ororientations outside a pre-specified range depending on the objectlocation, as discussed in detail below. The presence of noise or randommotion of objects other than the ones being tracked may lead to otherblobs besides 400, 402, 406, 408, 412, and 414. Also, the blobs of theobjects 140 being tracked may not be contiguous or may have internalholes. An adequate structuring element 164 can eliminate spurious blobsand internal holes. In morphology, a structuring element 164 determinesa shape used to interact with a given image. For example, a structuringelement 164 of a given width and height can be used as an erosion oropening element on a binary mask 142 containing identified foreground ormoving objects so that objects with width or height smaller than thoseof the structuring element 164 will be eliminated from the mask 142.Similarly, holes within an object 140 may be removed with themorphological operations of dilation or opening, with a structuringelement 164 greater than the dimensions of the holes. Morphologicalopening and closings with structuring elements 164 are often used inconjunction to remove spurious objects and holes within a binary mask142. In the context of the subject application, the expected dimensionsand orientation of an object and noise-induced holes as a function ofits location 148 within the field of view of the camera 134 can be usedto determine the appropriate dimensions and orientation of thestructuring elements 164 used in the different morphological operationsthat follow a frame-differencing or background subtraction operation.Note that the attributes of the structuring elements 164 used inmorphological operations being performed may be spatially dependent.

According to other aspects, computer vision techniques for objectrecognition and localization can be used on still images. It will beappreciated that such techniques entail a training stage wherein theappearance of multiple sample objects in a given feature space (e.g.,Harris Corners, scale invariant feature transform (SIFT), histogram oforiented gradients (HOG), local binary patterns (LBP), etc.) may be fedto a classifier (e.g., support vector machines (SVM), expectationmaximization (EM), neural networks, k nearest neighbors (k-NN), otherclustering algorithms, etc.) that may be trained on the available samplefeature representations. The trained classifier can then be applied tofeatures extracted from frames of interest and perform detection ofobjects of interest in the scene in a stand-alone manner; alternatively,it can be used in conjunction with the aforementioned motion andforeground detection techniques and determine if the initially detectedmotion blob is the object of interest with high probability. In eithercase, the parameters of bounding boxes (e.g., location, width andheight) surrounding the matching candidates can be output.

The instructions 108 stored in memory 106 may also include an objectcharacterization unit 114 that is configured to establish a targetobject representation of the image area determined by the objectdetection unit 112 to contain an object of interest 140. In one aspectof the subject embodiments, color features of the kernel 146 associatedwith the detected object 140 are used to represent an object in motion.For example, a 16-bin, three-dimensional histogram 500 of the RGB pixelvalues within the region where motion is detected is constructed. FIG. 5shows the histogram 500 corresponding to the object 140, i.e., the firstvehicle, detected in FIG. 4A. For visualization purposes, the 16³-colortensor has been vectorized into a 4096 dimensional vector.

Other feature representations, including texture appearance (LBPhistograms), gradient magnitude (HOG) and clouds of point descriptorssuch as Harris Corners, SIFT and SURF, may be utilized in accordancewith varying aspects of the subject embodiments. It will be appreciatedthat the object representation of an image region or kernel 146 may behighly dependent on its location, size and orientation, and the systemsand methods set forth herein utilize the selection of appropriate kernelparameters for tracking. The object characterization unit 114 receivesthe current frame 144 and a corresponding binary image 142 containingthe pixel location of foreground or moving objects that have beenclassified as valid objects by the object detection unit 112. The objectcharacterization unit 114 extracts features from the current frame 144at the locations indicated by the binary image 142, and communicatesthis set of features of the object(s) of interest 140 detected in thecurrent frame 144 to the object localization unit 116. It then forwardsthe location information of the identified valid objects to be trackedto the size and orientation determination unit 118, which, based on thereceived data, determines the appropriate, i.e., apparent, size andorientation of the kernel 146 and transmits it to the objectcharacterization unit 114.

FIGS. 6A-6E illustrates the need for a size and orientation dependentkernel 146. As shown, FIG. 6A depicts the image region (e.g., the kernel146) in which an initial object representation was computed for avehicle. FIGS. 6B and 6C show the image region utilized by previoustracking implementations that does not adapt the size or orientation ofthe tracking kernel 146. That is, it will be appreciated that thetracking kernels 146 on the objects 140 remain of the same size andorientation in both FIGS. 6B and 6C while the actual size andorientation of the objects 140 have changed. In contrast, according toone aspect of the subject embodiments, FIGS. 6D and 6E illustrate thesize and orientation adaptability of the kernel 146 utilizing thesystems and methods set forth herein, e.g., the size and orientation ofthe kernels 146 change in conjunction with the size and orientation ofthe objects 140. It will be appreciated that, given the significanteffect of perspective and distortion, the initial characterization ofthe objects includes the full body of the vehicle. It will further beappreciated that previous systems and methods for object tracking failedto adapt to changes in perceived size or orientation of the object beingtracked, and as such would subsequently sample significantly differentareas than those corresponding to the object of interest, therebyleading to errors in tracking. For example, the initial representationof the vehicle from FIG. 6A may contain information about the windows ofthe vehicles, whereas the representation illustrated in FIG. 6C may not.It will further be appreciated the initial representation from FIG. 6Amay contain little background information, whereas a significant portionof the background may be captured by the tracker in FIG. 6B.

Returning to FIG. 1, the instructions 108 further include the objectlocalization unit 116 that is configured to determine the location 148of the object 140 being tracked on a frame-by-frame basis via findingthe candidate kernel 146 with the appearance that best matches theappearance of the target kernel 146. That is, the object localizationunit 116 is configured to find the location 148 of the candidate object140 whose representation best matches the target object 140representation computed by the object characterization unit 112.

In accordance with one aspect of the subject embodiments, the objectlocalization unit 116 may utilize two methodologies in performing theaforementioned search for candidate objects 140 that best matchcorresponding target objects in the captured video 138. Combinations ofboth methodologies are also possible. The first methodology capable ofimplementation by the object localization unit 116 utilizes a searchprocess that assumes that the object location, size and orientationchange smoothly across frames, and the searches are performed forcandidate objects with the current size and orientation. After thelocation 148 of the best matching candidate is determined, its size 150and orientation 152 can be adjusted based upon input from the size andorientation unit 118. In this case, exchange of information between thelocalization unit 116 and the size and orientation unit 118 occurs atleast twice, once at the beginning of the search, and once at the end ofthe search.

The second methodology capable of implementation by the objectlocalization unit 116 utilizes a search process that is constantly awareof the predominant size 150 and orientation 152 of the candidate searchlocation 148, and, at every iteration of the search process, transmitsthe location of the candidate kernel 146 to the size and orientationunit 118. Responsive thereto, the object localization unit 116 receivesthe expected size 148 and orientation 150 of the candidate kernel 146,according to its location 152. For example purposes, the operation ofthe object localization unit 116 may be illustrated in the context oftemplate matching, point tracking, mean-shift tracking, and particlefilter tracking. However, it will be appreciated that the subjectsystems and methods are equally adaptable to other object trackingmethodologies utilizing the optimization techniques set forth herein.

With respect to template-matching tracking, operations are performed bysearching for the best match in terms of a similarity metric between thetemplate and a set of candidate samples. In contrast to mean shifttracking (discussed below), which performs iterative searches, templatematching performs an exhaustive search within the neighborhood ofinterest. Accordingly, template-matching tracking may begin with therepresentation of a sub-image of a given size and orientation centeredat a detected motion blob corresponding to the object 140 to be trackedat the initial frame 144. For the subsequent frames within theneighborhood of interest, normalized correlations between the templaterepresentation and the representations of the candidate windows of thecurrent frame 144 are calculated; the position where the maximalnormalized correlation occurs is considered as the position of thetracked object 140 in the current frame 144. The size and orientationunit 118 can perform correlations between the current objectrepresentation and candidate object representations at differentneighboring locations, each of which can be associated with a region ofa given size and orientation, as determined by the size and orientationunit 118. Iterations of this procedure are then performed until thetracking of the current object 140 is completed (e.g., when the object140 leaves the scene or is outside of region of interest). Additionally,the template may be updated from frame to frame using a sub-imagecentered at the current tracked position and with a specific size 150and orientation 152, again as determined by the size and orientationdetermination unit 118.

With respect to point tracking, features identifying salient points inthe region of interest (e.g., kernel 146) corresponding to the object140 being tracked are extracted, and individual point or groupcorrespondences are found across adjacent frames. Such features include,but are not limited to SIFT, SURF, Harris Corners, and KLT features. Inone embodiment, as correspondences are found between a set of featuresextracted from two instances of one object being tracked acrosstemporally adjacent frames 144, an affine consistency check between bothsets of features is performed by the size and orientation unit 118. Thischeck is performed to verify that the relative spatial location betweenboth sets of features is consistent both with the tracked object motion,as well as with the anticipated change in size 150 and orientation 152.Specifically, the affine transformation describing the changes undergoneby the feature set between adjacent frames is checked for consistencywith the expected change in size and orientation of the object relativeto its change in location.

With respect to mean-shift tracking, operations are performed byiteratively maximizing a similarity metric (e.g., BhattacharyyaCoefficient) between the target color histogram representation and a setof candidate histogram representations in a neighborhood centered at thecurrent location of the target, i.e., a region of interest (e.g., kernel146) in the frame 144. A suitable example of a histogram 500 is depictedin FIG. 5, as discussed above. As will be appreciated, instead ofexhaustively searching across all possible candidates, mean-shift isconfigured to estimate the gradient of the similarity metric andperforms a gradient ascent algorithm that is capable of maximizing thesimilarity between the target histogram representation and thecandidates (i.e., the histogram representations of the candidates) inthe search area. In accordance with one embodiment, the size 150 andorientation 152 of the object 140 varies smoothly between temporallyadjacent frames 144, whereby mean-shift can be performed at the localscale and orientation to find the location 148 of the best matchingcandidate kernel 146. Subsequently, the size 150 and orientation 152 ofthe kernel 146 are updated according to its new location 148.

With respect to particle filter tracking, operations are performed byestimating a probability density of the state of the system, whichtypically includes (but may not be limited to) the location of theobject being tracked. This density may be represented as a set ofweighted samples or particles. The set of particles contains more weightat locations where the object 140 being tracked is more likely to be.Knowledge about the object size 150 and location 148 can be used in asampling stage of the subject methodology, where the number and spatialdistribution of particles disseminated across a particular region can beadjusted according to the expected object shape, including its size andorientation.

The instructions 108 further include the object size and orientationunit 118 configured to determine the size and orientation of thetracking kernel 146 as a function of its location within the image,i.e., the video frame 144. The dependence of the object size on itslocation can be performed in several ways. In one implementation, ifinformation regarding the geometric setup of the camera 134 (i.e., thecamera's height above the ground and angle between the optical axis andthe vector to the ground) along with its intrinsic parameters is known(i.e., the geometric mapping function of a lens, such as a fisheyelens), the apparent size of the objects 140 can be estimated a priorivia camera calibration techniques, particularly under known constraintsof motion (e.g., vehicles are on the ground). The a priori informationutilized by the object size and orientation unit 118 may be determinedfrom historical information, e.g., past size 150 and orientation 152 ofobjects of interest 140 stored in an associated data storage 128, via aremote source 160 in data communication with the computer system 102 viaa suitable communications link 162, or the like. The remote source 160may comprise sizes 150 and orientations 152 corresponding to theregularized motion environment 141 in which the camera 134 ispositioned, as well as environment rules 156, image capture deviceinformation 158, and the like. The communications link 162 may comprise,for example, wired or wireless links, the Internet, an intranet, or thelike.

An example of such an estimation for a fisheye camera 134 is shown inFIGS. 7A-7B. FIG. 7A shows a camera 700 mounted above the ground 702 andan abstraction (i.e., a representation) of a vehicle, represented by arectangular prism 704 on the ground 702, i.e., the road, parking lot,etc. From a priori knowledge of the road, the angle of the car 704 onthe road surface 702 can be estimated. From knowledge of the mappingfunction of the lens, each point at the corner of the rectangular prism704 can be mapped to a pixel on the camera 700. The inputs to themapping function are the height of the camera 700 above the road 702 andthe coordinates of the vehicle, i.e., the prism 704 relative to thecamera 700. FIG. 7B provides an illustration of a mapping of corners ofthe rectangular prism 704 to an image plane. As shown in FIG. 7B, thearea of the convex hull of the 8 corners of the rectangular prism 704,mapped to the imaging plane of the camera 700 gives the estimated areaof the vehicle represented by the prism 704 at this position in thefield of view.

A sample result of this calculation for the fisheye camera is shown inFIG. 8A. The coordinates of the plot give the coordinates of a pixel ofthe camera that detects the object. Note that as in FIGS. 3, 4, and 6, afisheye lens field of view is captured in a circular area on the imageplane. For a given point in the 2-D plot, the magnitude of the value atthat point gives the relative size of the object if it is detected atthat particular location in the field of view. For example, if thevehicle, i.e., the rectangular prism 704, is in the lower right portionof the image (dark red), it will take up twice as much area in the imageplane as compared to if it is located in the green areas of the image.

In accordance with one aspect, the expected size and orientation of theobjects can be learned over time by performing object detectionrepeatedly and storing the pixel size 150 and orientation 152 of thedetected objects 140 as a function of their location 148, e.g., theobject information 154 of the associated data storage device 128. FIG.8A shows a pseudo-colored object size map corresponding to the camera134 and scene used in the experimental setup and obtained viacalibration. FIG. 8B shows the learned orientation pattern for theexample scenario described above. The orientation 152 in FIG. 8B can beused or calculated a priori from the known motion pattern along with theknown shape and dimension of the object 140 being tracked (e.g., currentor previously generated object information 154) to provide a moreaccurate estimation of the silhouette used in the calculation that gaveFIG. 8A. For example, while both the apparent size 150 and orientation152 of moving vehicles 140 change in the scenario under consideration,orientation of the corresponding kernel 146 would change little in thecase of pedestrian tracking; in that scenario, the perspective anddistortion would mainly affect the kernel size—that is, as long aspedestrians are always standing.

The computer system 102 may include one or more input/output (I/O)interface devices 119 and 120 for communicating with external devices.The I/O interface 119 may communicate, via communications link 132, withone or more of a display device 124, for displaying information such asreturned images, search results, object identification, video framestills, queries, and the like, and a user input device 126, such as akeyboard or touch or writable screen, for inputting text, and/or acursor control device, such as a mouse, trackball, or the like, forcommunicating user input information and command selections to theprocessor 104.

The various components of the computer system 102 associated with thesystem 100 may all be connected by a data/control bus 122. The processor104 of the computer system 102 is in communication with associated datastorage device 128 via a communications link 130 coupled to the I/Ointerface 119. A suitable communications link 130 may include, forexample, the public-switched telephone network, a proprietarycommunications network, infrared, optical, or other suitable wired orwireless data communications channel. The data storage device 128 iscapable of implementation on components of the computer system 102,e.g., stored in local memory 106, i.e., on hard drives, virtual drives,or the like, or on remote memory accessible to the computer system 102.

The associated data storage device 128 corresponds to any organizedcollection of data (e.g., video files, binary outputs, kernels, objects,etc.) used for one or more purposes. Implementation of the associateddata storage device 128 is capable of occurring on any mass storagedevice(s), for example, magnetic storage drives, a hard disk drive,optical storage devices, flash memory devices, or a suitable combinationthereof. The associated data storage 128 may be implemented as acomponent of the computer system 102, e.g., resident in memory 106, orthe like. In one embodiment, the associated data storage device 128 maystore video 138 acquired by the video acquisition unit 110 from thevideo camera 138. The data storage device 128 may further store objectinformation 154 comprising pixel size 148, orientation 150 and location152 data corresponding to one or more objects of interest 140 in aparticular video 138 or video frame 144. The data storage device 128 mayfurther store rules 156 corresponding to one or more regularized motionenvironments 141, e.g., speed limits, size restrictions, traffic flow,etc. According to one embodiment, video acquisition device information158 is also stored in the associated data storage device 128 that mayinclude, for example, the type of video camera 134, the lens used, thelocation of the camera 134 relative to the regularized motionenvironment 141, the frame rate, resolution, etc.

It will be appreciated that the video-based system 100 for tracking anobject of interest illustrated in FIG. 1 is capable of implementationusing a distributed computing environment, such as a computer network,which is representative of any distributed communications system capableof enabling the exchange of data between two or more electronic devices.It will further be appreciated that such a computer network includes,for example and without limitation, a virtual local area network, a widearea network, a personal area network, a local area network, theInternet, an intranet, or any suitable combination thereof. Accordingly,such a computer network comprises physical layers and transport layers,as illustrated by various convention data transport mechanisms, such as,for example, Token-Ring, Ethernet, or other wireless or wire-based datacommunication mechanisms. Furthermore, while depicted in FIG. 1 as anetworked set of components, the systems and methods discussed hereinare capable of implementation on a stand-alone device adapted to performthe methods described herein.

The computer system 102 may include a computer server, workstation,personal computer, cellular telephone, tablet computer, pager,combination thereof, or other computing device capable of executinginstructions for performing the exemplary method. According to oneexample embodiment, the computer system 102 includes hardware, software,and/or any suitable combination thereof, configured to interact with anassociated user, a networked device, networked storage, remote devices,or the like.

The memory 106 may represent any type of non-transitory computerreadable medium such as random access memory (RAM), read only memory(ROM), magnetic disk or tape, optical disk, flash memory, or holographicmemory. In one embodiment, the memory 106 comprises a combination ofrandom access memory and read only memory. In some embodiments, theprocessor 104 and the memory 106 may be combined in a single chip. Thenetwork interfaces 119 and/or 120 may allow the computer system 102 tocommunicate with other devices via a computer network, and may comprisea modulator/demodulator (MODEM). Memory 106 may store data processed inthe method as well as the instructions for performing the exemplarymethod.

The digital processor 104 can be variously embodied, such as by a singlecore processor, a dual core processor (or more generally by a multiplecore processor), a digital processor and cooperating math and/orgraphics coprocessor, a digital controller, or the like. The digitalprocessor 104 in addition to controlling the operation of the computersystem 102, executes the instructions 108 stored in the memory 106 forperforming the method outlined in FIGS. 9-10.

The term “software,” as used herein, is intended to encompass anycollection or set of instructions executable by a computer or otherdigital system so as to configure the computer or other digital systemto perform the task that is the intent of the software. The term“software,” as further used herein, is intended to also encompass suchinstructions stored in storage mediums, such as RAM, a hard disk,optical disk, or so forth, and is intended to encompass so-called“firmware” that is software stored on a ROM or so forth. Such softwaremay be organized in various ways, and may include software componentsorganized as libraries, Internet-based programs stored on a remoteserver or so forth, source code, interpretive code, object code,directly executable code, and so forth. It is contemplated that thesoftware may invoke system-level code or calls to other softwareresiding on a server or other location to perform certain functions.

Turning now to FIG. 9, there is provided an overview of the exemplarymethod for optimizing video-based tracking of an object of interest. Themethod 900 begins at 902, whereupon the computer system 102 generates abinary mask 142 of a detected instance of an object of interest 140 inone of a plurality of video frames 144. As discussed above, the objectof interest 140 may be detected via a plurality of different meansassociated with a current or previously acquired video 138 of aregularlized motion environment 141. In one embodiment, the detectedinstance of the object of interest 140 may be at a known location in thefield of view of a camera 134, e.g., a prepositioned sensor, gate, orthe like. Upon activation of the sensor or gate, an object of interest140 would be “detected” along with an initial position of the object ofinterest 140, based upon the geometry of the camera and the position ofthe sensor, gate, or the like. Thereafter, operations would proceed togenerate the binary mask 142 as depicted in FIG. 9.

A target object representation of the detected instance of the object ofinterest 140 is then established at 904 in accordance with the generatedbinary mask 142. At 906, the location 148 of the object of interest 140in the frame 144 is determined in accordance with the targetrepresentation of the detected instance of the object of interest 140.

At 908, an expected size and an expected orientation of the object ofinterest 140 is estimated as a function of the location of the object inthe frame 144. That is, the size and orientation unit 118 determines anapparent or expected size 150 and orientation 152 of the object 140using the location 148 of the object 140 in the frame 144, the positionof the camera 134 relative to the regularized motion environment 141,and the like. At 910, the object of interest 140 is localized in atleast one subsequent frame 144 of the video 138 using the expected size150 and orientation 152, thereby enabling tracking of the object ofinterest 140 in the video 138. Thereafter, at 912, the track of theobject of interest 140 in the acquired video 138 is output by thecomputer system 102 whereupon operations with respect to FIG. 9terminate.

Turning now to FIG. 10, there is shown an expanded view of the optimizedmethod 1000 for video-based tracking according to an exampleimplementation of the subject application. It will be appreciated thatthe order set forth hereinafter of the various steps in FIG. 10 areintended to illustrate one possible flow of operations of theaforementioned methodology. Accordingly, the various steps may beperformed sequentially, in parallel, or in any manner of order as willbe appreciated and as illustrated in FIG. 2, such that outputs of one ormore of the units 110-118 may be used as inputs by successive orpreceding units. In accordance with the example implementation, thevideo 138 referenced hereinafter is collected by an image capturedevice, i.e., via a video camera 134 employing a fish-eye lens. It willbe appreciated that other lens/camera combinations may also be utilizedin accordance with the systems and methods set forth in the subjectapplication. The method begins at 1002, whereupon the video acquisitionunit 110 acquires video 138 from the video camera 134 of a regularizedmotion environment 141, e.g., a parking lot, highway, drive-through, orthe like.

At 1004, the computer system 102 or other suitable component associatedwith the system 100 identifies the regularized motion environment 141from which the video 138 is acquired. Rules 156 corresponding to theidentified regularized motion environment 141 are then retrieved fromthe associated data storage device 128 at 1006. At 1008, videoacquisition device information 158 is retrieved from the associated datastorage 128 corresponding to the type of camera 134, the lens used, theknown location of the camera 134 relative to the regularized motionenvironment 141, and the like.

One or more objects of interest 140 are then detected in a frame 144 at1010 via the object detection unit 112 stored in instructions 108 of thecomputer system 102. In one embodiment, the object detection unit 112 isconfigured to utilize the known rules 156 and image capture deviceinformation 158 to assist in detecting objects of interest 140 in aninitial video frame 144 of the acquired video 138. For example, therules 156 may generally indicated to the unit 112 a location in theenvironment 141 in which an object 140 could or could not be found, andthe device information 158 utilized by the unit 112 in color processing,lighting or distortion effects, and the like. The object detection unit112 then generates a binary mask 142 corresponding to the detectedobject(s) of interest 140, e.g., corresponding to the motion/foregroundblobs of an object of interest 140 at 1012, and communicates the mask142 to the object characterization unit 114.

In accordance with one embodiment, a double difference techniquefollowed by morphological operations may be implemented by the objectdetection unit 112 to detect the initial instance of an object ofinterest 140 in the incoming video 138. The morphological operationsdiscard objects in motion with size and orientation outsidepre-determined ranges determined by the object size and orientationdetermination 118. In one embodiment, structuring elements 164, as willbe appreciated, are received from the object size and orientation unit118 by the object detection unit 112 to generate the mask 142 at 1012.See, e.g., the discussion of FIGS. 4A-4F above. As previously addressed,other methodologies for object recognition and localization may beutilized by the object detection unit 112 in accordance with the systemsand methods set forth herein, e.g., training methodologies, etc.

In accordance with one embodiment, the object size and orientation unit118 or other suitable component associated with the system 100 maygenerate structuring elements 164 for morphological operations duringthe mask creation at 1012. Such structuring elements 164 may beascertained from the expected size and orientation determined by theobject size and orientation unit 118 in accordance with a prioriinformation, as discussed above. It will be appreciated that themorphological operations perform hole-filling in masks that result fromthe initial thresholding operation, as well as removal of identifiedobjects with sizes and/or orientations outside a pre-specified rangedepending on the object location, as discussed above with respect toFIGS. 4A-4F. It will be further be appreciated that the structuringelements 164 for mask creation may be communicated to the objectdetection unit 112 for use on the next frame 144 of the captured video138 to track the object(s) of interest 140 from frame to frame asperformed by the object localization unit 116. In another embodiment,the structuring elements 164 correspond to points on a prism, asillustrated with respect to FIGS. 7A-7B discussed above.

Returning to FIG. 10, at 1014, the object characterization unit 114receives the binary mask 142 from the object detection unit 112 andestablishes a target object representation of the kernel 146 containingthe detected object(s) of interest 140 from the binary mask 142 and thecurrent frame. As discussed above, the object characterization unit 114may utilize color features of the kernel 146 to represent an object inmotion, e.g., FIG. 5, or other salient features of the object ofinterest 140, e.g., edge line features, texture-type features, cornerpoints, etc., as color features may change of an object 140 based upondirect or indirect lighting, shadow occlusions, and the like. The objectcharacterization unit 114 may also receive kernel size 150 andorientation 152 information from the object size and orientation unit118 for use in establishing the target kernel 146 from the binary mask142, as discussed above. Furthermore, as previously discussed, theobject characterization unit 114 is in communication with the objectlocalization unit 116 and the object size and orientation unit 118, suchthat the target object representation of the kernel 146 is communicatedthereto.

At 1016, the object localization unit 116 receives the target objectrepresentation of the kernel 146 in the video frame 144 and identifies acandidate kernel(s) in the video frame 144 that matches the targetkernel(s) 146. That is, the object localization unit determines thelocation 148 of the candidate object 140 whose representation bestmatches the target object 140 representation computed by the objectcharacterization unit 112. The location 148 of this candidate kernel 146is then communicated to the object size and orientation unit 118.

At 1022, the object size and orientation unit 118 retrieves historicalsize 150 and orientation 152 information from the data storage device128 for use in determining the expected orientation and expected size ofa candidate kernel 146 as discussed above. At 1024, the object and sizeorientation unit 118 may retrieve, via a suitable communications linkand network (e.g., the Internet), size and orientation information froma third party remote source 160. It will be appreciated that steps 1022and 1024 are included for example purposes. The methodology 1000 of FIG.10 may use either, both, or neither sources of information indetermining the expected size and orientation of a candidate kernel in anext subsequent frame 144.

Thereafter, at 1026, the object size and orientation unit 118 or othersuitable component of the system 100 determines, via at least one ofcalculations or via the a priori knowledge of 1022 or 1024, the expectedsize 150 and orientation 152 of a candidate kernel 146 in a nextsubsequent frame 144. That is, the object size and orientation unit 118estimates the size 150 and orientation 152 of a candidate kernel 146 asit should appear in the next subsequent frame 144 based upon the apriori knowledge or upon calculations utilizing the location 148thereof. For example, the object size and orientation unit 118 is awareof the location of the camera 134 and the previous trajectory (size andorientation) of the object of interest 140 in the current frame and isthereby configured to calculate the size 150 and orientation 152 of theobject of interest 140 in the next subsequent frame 144.

A determination is then made at 1028 whether another frame 144 in thevideo 138 remains for processing according to the methodology 1000 ofFIG. 10, e.g., the video 138 has finished running, no objects 140detected, or the like. Upon a positive determination, operations returnto 1010 for detection of the object(s) of interest 140 in the nextsubsequent video frame 144 by the object detection unit 112. It will beappreciated, however, that the subsequent analysis of frames 144 in thevideo 138 the apparent kernel size and orientation, and the expectedsize and orientation generated by the object size and orientation unit118, thereby optimizing the tracking of objects of interest 140 in theacquired video 138. Operations continue thereafter as set forth abovewith respect to 1012-1028.

Upon a determination at 1028 that no additional frames 144 remain foranalysis in accordance with FIG. 10, operations proceed to 1030. At1030, the optimized tracked object of interest trajectory in theacquired video 138 is output. For example, the output may be sent to thedisplay device 124 in communication with the computer system 102, sentto the data storage device 128 for later review, communicated via anetwork to an external site, or the like.

The method illustrated in FIGS. 9-10 may be implemented in a computerprogram product that may be executed on a computer. The computer programproduct may comprise a non-transitory computer-readable recording mediumon which a control program is recorded (stored), such as a disk, harddrive, or the like. Common forms of non-transitory computer-readablemedia include, for example, floppy disks, flexible disks, hard disks,magnetic tape, or any other magnetic storage medium, CD-ROM, DVD, or anyother optical medium, a RAM, a PROM, an EPROM, a FLASH-EPROM, or othermemory chip or cartridge, or any other tangible medium from which acomputer can read and use.

Alternatively, the method may be implemented in transitory media, suchas a transmittable carrier wave in which the control program is embodiedas a data signal using transmission media, such as acoustic or lightwaves, such as those generated during radio wave and infrared datacommunications, and the like.

The exemplary method may be implemented on one or more general purposecomputers, special purpose computer(s), a programmed microprocessor ormicrocontroller and peripheral integrated circuit elements, an ASIC orother integrated circuit, a digital signal processor, a hardwiredelectronic or logic circuit such as a discrete element circuit, aprogrammable logic device such as a PLD, PLA, FPGA, Graphical card CPU(GPU), or PAL, or the like. In general, any device, capable ofimplementing a finite state machine that is in turn capable ofimplementing the flowchart shown in FIGS. 9-10, can be used to implementthe method estimating origins and destinations for users of atransportation system.

It will be appreciated that variants of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be combined intomany other different systems or applications. Various presentlyunforeseen or unanticipated alternatives, modifications, variations orimprovements therein may be subsequently made by those skilled in theart which are also intended to be encompassed by the following claims.

What is claimed is:
 1. A method for optimizing video-based tracking ofan object of interest, comprising: acquiring, from a fixed positionvideo camera having a wide-view lens, a video of a regularized motionenvironment comprising a plurality of video frames, the regularizedmotion environment having a stationary scene background; retrieving aset of rules of the regularized motion environment, the rules governingmovement of an object within the regularized motion environmentincluding at least two of a speed limit, a size restriction, a trafficflow, and/or a location in the regularized motion environment wherein anobject of interest can be located or a location in the regularizedmotion environment wherein an object of interest cannot be located;detecting an initial instance of at least one object of interest in theplurality of video frames including a location thereof in accordancewith the retrieved set of regularized motion environment rules;estimating both an expected size and an expected orientation of the atleast one object of interest as a function of the location and ageometric mapping function of the wide-view lens of the fixed positionvideo camera acquiring the video; and localizing the at least one objectof interest in at least one subsequent video frame responsive to theestimated size and estimated orientation, wherein at least one of theacquiring, detecting, generating, determining, and localizing isperformed by a computer processor.
 2. The method according to claim 1,further comprising: generating a binary mask of the at least one objectof interest in a current video frame; and generating a targetrepresentation of the at least one object of interest in accordance withthe binary mask and the current video frame.
 3. The method according toclaim 2, wherein the expected size and the expected orientation of theat least one object of interest is determined in accordance with ageometry of a video camera acquiring the video of the regularized motionenvironment.
 4. The method according to claim 2, further comprisinggenerating a plurality of structuring elements in accordance with theexpected size and orientation of the at least one object of interest asa function of the location, wherein morphological operations areperformed on the binary mask in accordance with the plurality ofstructuring elements.
 5. The method according to claim 1, wherein theexpected size and orientation of the at least one object of interest asa function of the location are determined in accordance with historicalinformation corresponding to size and orientation, wherein thehistorical information is associated with the regularized motionenvironment.
 6. The method according to claim 1, wherein the expectedsize and orientation of the at least one object of interest as afunction of the location are determined in accordance a remote source ofinformation corresponding to size and orientation, wherein the remotesource is associated with the regularized motion environment.
 7. Themethod according to claim 1, wherein localizing the at least one objectuses at least one of a template matching tracking, point featuretracking, a mean shift tracking, or a particle filter tracking.
 8. Asystem for optimizing video-based tracking of an object of interest,comprising: a video acquisition unit configured for acquiring, from afixed position video camera having a wide-view lens, a video of aregularized motion environment in memory, the video comprising aplurality of frames, wherein the regularized motion environment having astationary scene background; an object detection unit configured fordetecting an initial instance of an object of interest a frame of theplurality of video frames in accordance with a set of rulescorresponding to the regularized motion environment, the rules governingmovement of an object within the regularized motion environmentincluding at least two of a speed limit, a size restriction, a trafficflow, and/or a location in the regularized motion environment wherein anobject of interest can be located or a location in the regularizedmotion environment wherein an object of interest cannot be located; anobject characterization unit configured for establishing a target objectrepresentation of the detected instance of the object of interest; anobject localization unit configured for determining a location of theobject of interest in the frame in accordance with the targetrepresentation of the detected instance of the object of interest; anobject size and orientation unit configured for estimating a size and anorientation of the object of interest in a next subsequent frame as afunction of the determined location and a geometric mapping function ofthe wide-view lens of the fixed position video camera acquiring thevideo; and a processor which implements at least one of the videoacquisition unit, the object detection unit, the object characterizationunit, the object localization unit, and the object size and orientationunit.
 9. The system according to claim 8, wherein the object size andorientation unit is further configured for generating a plurality ofstructuring elements in accordance with the estimated size andorientation of the object of interest as a function of location in thenext subsequent frame.
 10. The system according to claim 9, wherein theobject size and orientation unit is further configured to estimate thesize and orientation of the at least one object of interest as afunction of the location in accordance with historical informationcorresponding to size and orientation, wherein the historicalinformation is associated with the regularized motion environment. 11.The system according to claim 9, wherein the object size and orientationunit is further configured to estimate the size and orientation of theat least one object of interest as a function of the location aredetermined in accordance a remote source of information corresponding tosize and orientation, wherein the remote source is associated with theregularized motion environment; and wherein the object detection unit isfurther configured for modifying a binary mask of the object of interestin accordance with the plurality of generated structuring elements. 12.The system according to claim 11, wherein the object characterizationunit establishes the target object representation of the detectedinstance of the object of interest in accordance with the generatedbinary mask and the current video frame.
 13. The system according toclaim 12, wherein the object localization unit is further configured todetermine the location in accordance with at least one of a templatematching tracking, a point feature tracking, a mean shift tracking, or aparticle filter tracking.
 14. A computer-implemented method foroptimizing video-based tracking of an object of interest, comprising:acquiring, from a fixed position video camera having a wide-view lens, avideo of a regularized motion environment comprising a plurality ofvideo frames, the regularized motion environment having a stationaryscene background; retrieving a set of rules corresponding to theregularized motion environment, the rules governing movement of anobject within the regularized motion environment including at least twoof a speed limit, a size restriction, a traffic flow, and/or a locationin the regularized motion environment wherein an object of interest canbe located or a location in the regularized motion environment whereinan object of interest cannot be located; detecting an instance of anobject of interest in one of a plurality of video frames in accordancewith the retrieved set of regularized motion environment rules;generating a binary mask of a detected instance of an object of interestin one of a plurality of video frames; establishing a target objectrepresentation of the detected instance of the object of interest inaccordance with the generated binary mask; determining a location of theobject of interest in the frame in accordance with the targetrepresentation of the detected instance of the object of interest and ageometric mapping function of the wide-view lens of the fixed positionvideo camera acquiring the video; estimating a size and an orientationof the object of interest as a function of the location; and localizingthe object of interest in a next subsequent frame responsive to theestimated size and orientation.
 15. The computer-implemented methodaccording to claim 14, wherein the estimated size and orientation of theat least one object of interest as a function of the location aredetermined in accordance with historical information or a remote sourceof information corresponding to size and orientation, wherein thehistorical information or the remote source of information is associatedwith the regularized motion environment.
 16. The computer-implementedmethod according to claim 15, further comprising generating a pluralityof structuring elements in accordance with the estimated size andorientation of the object of interest as a function of location in thenext subsequent frame, wherein the binary mask is modified in accordancewith the plurality of structuring elements, and wherein morphologicaloperations are performed on the binary mask in accordance with theplurality of structuring elements.
 17. The computer-implemented methodaccording to claim 16, wherein the estimated size and orientation of theobject of interest are estimated in accordance with a geometry of avideo camera acquiring the video of the regularized motion environment.18. The computer-implemented method according to claim 17, furthercomprising: detecting the instance of the object of interest in the oneof the plurality of video frames.
 19. The method according to claim 1,wherein the rules are representative of at least one of a speed limit, asize restriction, a traffic flow, a location in the regularized motionenvironment wherein an object of interest can be located or a locationor a location in the regularized motion environment wherein an object ofinterest cannot be located.
 20. The system according to claim 8, whereinthe rules are representative of at least one of a speed limit, a sizerestriction, a traffic flow, a location in the regularized motionenvironment wherein an object of interest can be located or a locationor a location in the regularized motion environment wherein an object ofinterest cannot be located.